The invention is particularly, but not exclusively, applicable to the control of a heating and/or cooling system, such as a central heating system or air conditioning system for the home or office. It is still the case that most domestic space heating systems control temperature by targeting a set point temperature (which may form part of a schedule of such temperatures that vary over a control period) and using a thermostat with hysteresis to turn the heat source on and off. This type of control is based only on the current state of the house and its heating system, and does not take into account any forward planning or knowledge of the thermal response of the house.
Consequently, the known method can fail to achieve energy efficiency and/or the best comfort for the occupants of the house, and provides the occupants practically no insight into the thermal performance of the house and the heating/cooling system, for example the rate of heat loss from the house to the external environment and the energy input to the heating/cooling system that will be required in order to achieve a desired temperature change within the house. These drawbacks are particularly pertinent to the control of a heat pump-based heating or cooling system.
There are existing systems which remotely measure room temperature and enable the home owner to view the recorded temperature traces by an internet page, but the services rarely provide any additional insight. Some existing services do provide some analysis (see for example https://www.myjoulo.com/) but not to the extent of taking into account the thermal properties of the house.
It has been proposed to control temperature within a building using a system in which the effect of an energy input into the building's heating or air conditioning system on temperature is predicted using data on the thermal characteristics of the building and on external conditions. In the paper “Optimization of a Heating System using Smart Meters and Dynamic Pricing” (Huval. XP002696078) it is proposed to determine a schedule for the heat to be supplied by a heating system by determining a control schedule of a hot water supply valve (that controls the supply of hot water to a heater) in order to minimise a cost function comprising the sum of the cost of heating the building and the cost of “loss of comfort”. However, the document does not discuss how the building thermal parameters, necessary for the predictive model, are determined and indeed indicates that it would be problematic for an individual household to find the necessary coefficients in order to set up an accurate model.
Certain control systems incorporate “optimum start” facilities, whereby the rate of increase of temperature with time is learned when the house is being heated up, and this gradient value is then used to subsequently calculate when to start heating in order for the house to get to a particular temperature at a particular time, and can thus perform a more accurate optimum start time calculation. Other systems learn simple characteristics in order to reduce heating system “undershoot” and “overshoot”, as a result of the hysteresis mentioned above (e.g. U.S. 2010/0096467). However, the systems cannot predict the thermal response of a house particularly accurately, especially where the temperatures of the house do not change significantly over time, for example the house heated by an underfloor heating system or a heat pump.